scholarly journals Single Cell RNA Sequencing Identifies Potential Molecular Indicators of Response to Melflufen in Multiple Myeloma

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1194-1194
Author(s):  
Philipp Sergeev ◽  
Sadiksha Adhikari ◽  
Juho J. Miettinen ◽  
Maiju-Emilia Huppunen ◽  
Minna Suvela ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Single Cell ◽  
Research Funding ◽  
Drug Sensitivity ◽  
Ex Vivo ◽  
Cell Populations ◽  
Drug Efflux ◽  
Single Cell Rna Sequencing ◽  
Differential Gene

Abstract Introduction Melphalan flufenamide (melflufen), is a novel peptide-drug conjugate that targets aminopeptidases and selectively delivers alkylating agents in tumors. Melflufen was recently FDA approved for the treatment of relapsed/refractory multiple myeloma (MM) patients. Considering the challenges in treating this group of patients, and the availability of several new drugs for MM, information that can support treatment selection is urgently needed. To identify potential indicators of response and mechanism of resistance to melflufen, we applied a multiparametric drug sensitivity assay to MM patient samples ex vivo and analyzed the samples by single cell RNA sequencing (scRNAseq). Ex vivo drug testing identified MM samples that were distinctly sensitive or resistant to melflufen, while differential gene expression analysis revealed pathways associated with response. Methods Bone marrow (BM) aspirates from 24 MM patients were obtained after written informed consent following approved protocols in compliance with the Declaration of Helsinki. BM mononuclear cells from 12 newly diagnosed (ND) and 12 relapsed/refractory (RR) patients were used for multi-parametric flow cytometry-based drug sensitivity and resistance testing (DSRT) evaluation to melflufen and melphalan, and for scRNAseq. Based on the results from the DSRT tests and drug sensitivity scores (DSS), we divided the samples into three groups - high sensitivity (HS, DSS > 40 (melflufen) or DSS > 16 (melphalan)), intermediate sensitivity (IS, 31 ≤ DSS ≤ 40 (melflufen) or 10 ≤ DSS ≤ 16 (melphalan)), and low sensitivity (LS, DSS < 31 (melflufen) or DSS < 10 (melphalan)). To identify genes, responsible for the general sensitivity to melphalan-based drugs we conducted differential gene expression (DGE) analyses separately for melphalan and melflufen focusing on the plasma cell populations, comparing gene expression between HS and LS samples for both drugs ("HS vs. LS melphalan" and "HS vs. LS for melflufen", respectively). In addition, to explain the increased sensitivity of RR samples, we conducted the DGE analysis for ND vs. RR samples and searched for similarities between these three datasets. Results DSRT data indicated that samples from RRMM patients were significantly more sensitive to melflufen compared to samples from NDMM (Fig. 1A). In addition, we observed that samples with a gain of 1q (+1q) were more sensitive to melflufen while those with deletion of 13q (del13q) appeared to be less sensitive, although these results lacked significance (Fig. 1A). After separating the samples into different drug sensitivity groups (HS, IS, LS), DGE analysis showed significant downregulation of the drug efflux and multidrug resistance protein family member ABCB9 in the melflufen HS group opposed to the LS group (2.2-fold, p < 0.001). A similar pattern was detected for the melphalan HS vs. LS comparison suggesting that this alteration might be a common indicator of sensitivity to melphalan-based drugs. Furthermore, in the melflufen HS group we observed downregulation of the matrix metallopeptidase inhibitors TIMP1 and TIMP2 (3-fold and 1.6-fold, p < 0.001, respectively), and cathepsin inhibitors CST3 and CSTB (3.2-fold and 1.3-fold, p < 0.001, respectively) (Fig. 1B). This effect was observed in both "ND vs. RR" and "HS vs. LS for melflufen" comparisons, but not for melphalan, suggesting that these changes are associated with disease progression and specific indicators of sensitivity to melflufen. Moreover, gene set enrichment analysis (GSEA) showed activation of pathways related to protein synthesis, as well as amino acid starvation for malignant and normal cell populations in the HS group. Conclusion In summary, our results indicate that melflufen is more active in RRMM compared to NDMM. In addition, samples from MM patients with +1q, which is considered an indicator of high-risk disease, tended to be more sensitive to melflufen. Based on differential GSEA and pathway enrichment, several synergizing mechanisms could potentially explain the higher sensitivity to melflufen, such as decreased drug efflux and increased drug uptake. Although these results indicate potential indicators of response and mechanisms of drug efficacy, further validation of these findings is required using data from melflufen treated patients. Figure 1 Figure 1. Disclosures Slipicevic: Oncopeptides AB: Current Employment. Nupponen: Oncopeptides AB: Consultancy. Lehmann: Oncopeptides AB: Current Employment. Heckman: Orion Pharma: Research Funding; Oncopeptides: Consultancy, Research Funding; Novartis: Research Funding; Celgene/BMS: Research Funding; Kronos Bio, Inc.: Research Funding.

scholarly journals Single-Cell RNA-Sequencing Identifies Activation of TP53 and STAT1 Pathways in Human T Lymphocyte Subpopulations in Response to Ex Vivo Radiation Exposure

2019 ◽  
Vol 20 (9) ◽  
pp. 2316 ◽  
Author(s):  
Maria Moreno-Villanueva ◽  
Ye Zhang ◽  
Alan Feiveson ◽  
Brandon Mistretta ◽  
Yinghong Pan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cluster Analysis ◽  
Dna Damage ◽  
Radiation Exposure ◽  
Single Cell ◽  
T Lymphocyte ◽  
Ex Vivo ◽  
Lymphocyte Subpopulations ◽  
Single Cell Rna Sequencing ◽  
Sequencing Technique

Detrimental health consequences from exposure to space radiation are a major concern for long-duration human exploration missions to the Moon or Mars. Cellular responses to radiation are expected to be heterogeneous for space radiation exposure, where only high-energy protons and other particles traverse a fraction of the cells. Therefore, assessing DNA damage and DNA damage response in individual cells is crucial in understanding the mechanisms by which cells respond to different particle types and energies in space. In this project, we identified a cell-specific signature for radiation response by using single-cell transcriptomics of human lymphocyte subpopulations. We investigated gene expression in individual human T lymphocytes 3 h after ex vivo exposure to 2-Gy gamma rays while using the single-cell sequencing technique (10X Genomics). In the process, RNA was isolated from ~700 irradiated and ~700 non-irradiated control cells, and then sequenced with ~50 k reads/cell. RNA in each of the cells was distinctively barcoded prior to extraction to allow for quantification for individual cells. Principal component and clustering analysis of the unique molecular identifier (UMI) counts classified the cells into three groups or sub-types, which correspond to CD4+, naïve, and CD8+/NK cells. Gene expression changes after radiation exposure were evaluated using negative binomial regression. On average, BBC3, PCNA, and other TP53 related genes that are known to respond to radiation in human T cells showed increased activation. While most of the TP53 responsive genes were upregulated in all groups of cells, the expressions of IRF1, STAT1, and BATF were only upregulated in the CD4+ and naïve groups, but were unchanged in the CD8+/NK group, which suggests that the interferon-gamma pathway does not respond to radiation in CD8+/NK cells. Thus, single-cell RNA sequencing technique was useful for simultaneously identifying the expression of a set of genes in individual cells and T lymphocyte subpopulation after gamma radiation exposure. The degree of dependence of UMI counts between pairs of upregulated genes was also evaluated to construct a similarity matrix for cluster analysis. The cluster analysis identified a group of TP53-responsive genes and a group of genes that are involved in the interferon gamma pathway, which demonstrate the potential of this method for identifying previously unknown groups of genes with similar expression patterns.

scholarly journals Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue

Circulation ◽  
2020 ◽  
Vol 142 (14) ◽  
pp. 1374-1388
Author(s):  
Yanming Li ◽  
Pingping Ren ◽  
Ashley Dawson ◽  
Hernan G. Vasquez ◽  
Waleed Ageedi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Aortic Wall ◽  
Genome Wide Association ◽  
Aortic Tissue ◽  
Sequencing Data ◽  
Genome Wide ◽  
Single Cell Rna Sequencing ◽  
Differential Gene

Background: Ascending thoracic aortic aneurysm (ATAA) is caused by the progressive weakening and dilatation of the aortic wall and can lead to aortic dissection, rupture, and other life-threatening complications. To improve our understanding of ATAA pathogenesis, we aimed to comprehensively characterize the cellular composition of the ascending aortic wall and to identify molecular alterations in each cell population of human ATAA tissues. Methods: We performed single-cell RNA sequencing analysis of ascending aortic tissues from 11 study participants, including 8 patients with ATAA (4 women and 4 men) and 3 control subjects (2 women and 1 man). Cells extracted from aortic tissue were analyzed and categorized with single-cell RNA sequencing data to perform cluster identification. ATAA-related changes were then examined by comparing the proportions of each cell type and the gene expression profiles between ATAA and control tissues. We also examined which genes may be critical for ATAA by performing the integrative analysis of our single-cell RNA sequencing data with publicly available data from genome-wide association studies. Results: We identified 11 major cell types in human ascending aortic tissue; the high-resolution reclustering of these cells further divided them into 40 subtypes. Multiple subtypes were observed for smooth muscle cells, macrophages, and T lymphocytes, suggesting that these cells have multiple functional populations in the aortic wall. In general, ATAA tissues had fewer nonimmune cells and more immune cells, especially T lymphocytes, than control tissues did. Differential gene expression data suggested the presence of extensive mitochondrial dysfunction in ATAA tissues. In addition, integrative analysis of our single-cell RNA sequencing data with public genome-wide association study data and promoter capture Hi-C data suggested that the erythroblast transformation-specific related gene( ERG ) exerts an important role in maintaining normal aortic wall function. Conclusions: Our study provides a comprehensive evaluation of the cellular composition of the ascending aortic wall and reveals how the gene expression landscape is altered in human ATAA tissue. The information from this study makes important contributions to our understanding of ATAA formation and progression.

scholarly journals Characterization of Patients with Multiple Myeloma in Long- Term Remission

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4508-4508 ◽  
Author(s):  
Raphael K. Lutz ◽  
Katharina Kriegsmann ◽  
Mohamed H.S. Awwad ◽  
Carsten Müller-Tidow ◽  
Gerlinde Egerer ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
Single Cell ◽  
Research Funding ◽  
High Dose ◽  
Disease Course ◽  
Monoclonal Protein ◽  
Single Cell Rna Sequencing ◽  
Indolent Disease

Abstract INTRODUCTION: Multiple Myeloma is still considered an incurable disease despite the development of new therapy options. However, there is a small fraction of patients achieving a long- term remission (LTR) after induction therapy followed by high dose chemotherapy and autologous stem cell transplantation (ASCT). Such patients that are still in complete remission or experience an indolent disease course over many years after high dose therapy are referred to as functionally cured. To date, it is still unclear which patients experience a long term disease control. METHODS: We have screened our Myeloma register for patients that experienced a LTR over 7 years after high dose chemotherapy followed by ASCT. Characteristics of patients that fit to these criteria have been analyzed in detail. The current disease state was evaluated according to the IMWG criteria. Using the Next Generation Flow technique from Cytognos, bone marrow samples from the patients were examined for minimal residual disease (MRD, sensitivity <10-5). To further characterize the bone marrow environment of myeloma patients in LTR, we currently perform a quantitative analysis of the lymphocyte compartment in peripheral blood and bone marrow using flow cytometry. Moreover, bone marrow mononuclear cells are currently being characterized using the single cell RNA sequencing approach by 10X Genomics. RESULTS: We have identified 24 living patients with ongoing remission from 7 till 17 years after high dose therapy and autologous stem cell transplantation. Patients' characteristics are summarized in Table 1. Unexpectedly, 10 patients had a poor prognosis score at first diagnosis (6 patients with ISS score II and 4 patients with ISS score III). Furthermore, the average tumor burden, determined by plasma cell infiltration of bone marrow at initial diagnosis, was remarkably high with 49.5 %. 4 patients had high risk cytogenetics (3 patients with TP53/del17p and 1 patient with t(4;14)). Regarding the depth of response, the 24 patients were subdivided into 3 groups (Table 2). 9 of 24 patients had a detectable monoclonal protein in serum with an indolent disease course. 15 of 24 patients had no detectable monoclonal protein in serum. Of note, MRD assessment of the bone marrow by Next Generation Flow revealed MRD positivity in 4 of 15 patients. Preliminary data using flow cytometry and single cell RNA sequencing suggest a unique immunological profile of the different patient cohorts in LTR. CONCLUSION: Patients with multiple myeloma in LTR can be subdivided into 3 groups: patients with detectable monoclonal protein but indolent disease course, patients in Flow MRD negative complete remission and patients in Flow MRD positive remission. A deep analysis of patients' peripheral blood and bone marrow by flow cytometry and single cell RNA sequencing is currently being performed focusing on the immunological signature of the different patient cohorts. Preliminary results suggest a unique immunophenotype which is possibly associated to long- term disease control. Data will be presented at the meeting. Disclosures Kriegsmann: BMS: Research Funding; Celgene: Research Funding. Raab:BMS: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding. Durie:Janssen: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Takeda: Consultancy. Goldschmidt:Takeda: Consultancy, Research Funding; Mundipharma: Research Funding; Novartis: Honoraria, Research Funding; Chugai: Honoraria, Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Research Funding; Adaptive Biotechnology: Consultancy; ArtTempi: Honoraria.

scholarly journals An accessible, interactive GenePattern Notebook for analysis and exploration of single-cell transcriptomic data

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1306 ◽  
Author(s):  
Clarence K. Mah ◽  
Alexander T. Wenzel ◽  
Edwin F. Juarez ◽  
Thorin Tabor ◽  
Michael M. Reich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Clustering Analysis ◽  
Cell Types ◽  
Cell Populations ◽  
Popular Method ◽  
Transcriptomic Data ◽  
Processing Data ◽  
Single Cell Rna Sequencing ◽  
Analysis Workflow

Single-cell RNA sequencing (scRNA-seq) has emerged as a popular method to profile gene expression at the resolution of individual cells. While there have been methods and software specifically developed to analyze scRNA-seq data, they are most accessible to users who program. We have created a scRNA-seq clustering analysis GenePattern Notebook that provides an interactive, easy-to-use interface for data analysis and exploration of scRNA-Seq data, without the need to write or view any code. The notebook provides a standard scRNA-seq analysis workflow for pre-processing data, identification of sub-populations of cells by clustering, and exploration of biomarkers to characterize heterogeneous cell populations and delineate cell types.

scholarly journals Combined Targeting of BET Family Proteins and BCL2 Is Synergistic in Acute Myeloid Leukemia Cells Overexpressing S100A8 and S100A9

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2634-2634
Author(s):  
Riikka Karjalainen ◽  
Minxia Liu ◽  
Ashwini Kumar ◽  
Alun Parsons ◽  
Liye He ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Drug Combination ◽  
Calcium Binding ◽  
Research Funding ◽  
Drug Sensitivity ◽  
Ex Vivo ◽  
High Expression ◽  
Bet Inhibitor ◽  
Combination Studies

Abstract Background The 5-year survival rate for acute myeloid leukemia (AML) remains poor with most patients succumbing to relapse or refractory disease. Recently, the BCL2 specific inhibitor venetoclax has shown promising anti-leukemia activity in high-risk AML patients. Most patients, however, ultimately develop resistance to monotherapy and novel combination treatments with venetoclax are needed for patients with no other therapy options available. In this study we identified high expression of calcium binding protein S100A8/S100A9 to be associated with venetoclax resistance and looked for drug combinations to overcome the resistance in AML patient samples overexpressing S100A8 and S100A9. Methods Gene expression was assessed by RNA sequencing of AML patient samples and validated by qPCR. Gene enrichment analysis was performed on differentially expressed genes between venetoclax highly sensitive (n=3) and resistant (n=4) samples. Sensitivity of AML patient derived mononuclear cells was assessed to 304 different small molecule inhibitors by measuring cell viability after 72 h incubation with 5 different concentrations (1-10,000 nM) using the CellTiter-Glo (CTG) assay. A drug sensitivity score (DSS) was calculated based on a modified area under the dose response curve calculation. Drug combination studies were performed using AML cell lines resistant to venetoclax and confirmed with primary patient cells (n=15). Data of the drug combination studies were analyzed with the Zero Interaction Potency (ZIP) model by considering a dose-response matrix where two drugs are tested at 8 concentrations in a serially diluted manner. Statistical dependence between gene expression and drug sensitivity data was assessed by Pearson's correlation coefficient modelling. Results Venetoclax resistant AML patient samples were found to overexpress genes related to immune responses including inflammatory related proteins S100A8 and S100A9. The expression of S100A8 and S100A9 was upregulated in a sub-group of AML patients with somatic mutations in DNA methylation genes IDH2 and TET2 and chromatin modifier ASXL1. Functional studies with AML cell lines validated high expression of the S100 proteins in cells insensitive to venetoclax (NOMO-1, SKM-1 and SHI-1) whereas sensitive cell lines (MOLM-13, Kasumi-1 and ML-2) did not express the proteins. Integrated analysis of S100A8 and S100A9 expression and ex vivo drug sensitivity data indicated positive correlation of S100 expression with sensitivity to BET inhibitor (birabresib), PI3K inhibitor (TG100-115) and MEK1/2 inhibitor (AZD8330). In contrast, sensitivity to venetoclax and the FLT3 inhibitor quizartinib negatively correlated with S100 gene expression. Subsequently, we combined positively correlating drugs with venetoclax and tested the efficacy of these combinations in AML cell lines and patient samples. From the drug combination studies we found that BET inhibitor birabresib was able to overcome resistance to venetoclax treatment. The BCL2/BET inhibitor combination was synergistic in venetoclax resistant cell lines NOMO-1 and SKM-1, which express high-levels of S100A8 and S100A9 (Figure 1A). Efficacy of the combination on primary AML patient samples correlated with the expression level of the S100 genes. Nine of 11 high expression samples were sensitive to the venetoclax/birabresib combination (Figure 1B-C), whereas no synergy was observed in 3 of 4 samples with a low level of S100 expression. Conclusions The calcium binding proteins S100A8 and S100A9 are abundant in myeloid cells and are associated with poor prognosis in AML (Edgeworth et al, J Biol Chem. 1991; Nicolas et al, Leukemia 2011). From ex vivo and in vitro analyses of AML, we found that high expression of S100A8 and S100A9 is highly correlative with resistance to the BCL2 inhibitor venetoclax. In contrast, high S100A8 and S100A9 expression correlates with sensitivity to BET inhibitor birabresib. Interestingly, our studies showed that these two drugs act synergistically in venetoclax resistant AML cell lines and AML patient samples and may be a beneficial novel combination that should be further confirmed in preclinical and clinical investigations. Disclosures Porkka: Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Heckman:Orion Pharma: Research Funding; Novartis: Research Funding; Celgene: Research Funding.

scholarly journals Scattome: A Single-Cell Analysis of Targeted Transcriptome Program to Predict Drug Sensitivity of Single Cells within Human Myeloma Tumors

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4249-4249
Author(s):  
Amit Kumar Mitra ◽  
Ujjal Mukherjee ◽  
Taylor Harding ◽  
Holly Stessman ◽  
Ying Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Lines ◽  
Research Funding ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Pcr Analysis ◽  
Cell Analysis

Abstract Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that likely plays a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Such heterogeneity is a driving factor in the evolution of MM, from founder clones through outgrowth of subclonal fractions. DNA Sequencing studies on MM samples have indeed demonstrated such heterogeneity in subclonal architecture at diagnosis based on recurrent mutations in pathologically relevant genes that may ultimately to lead to relapse. However, no study so far has reported a predictive gene expression signature that can identify, distinguish and quantify drug sensitive and drug-resistant subpopulations within a bulk population of myeloma cells. In recent years, our laboratory has successfully developed a gene expression profile (GEP)-based signature that could not only distinguish drug response of MM cell lines, but also was effective in stratifying patient outcomes when applied to GEP profiles from MM clinical trials using proteasome inhibitors (PI) as chemotherapeutic agents. Further, we noted myeloma cell lines that responded to the drug often contained residual sub-population of cells that did not respond, and likely were selectively propagated during drug treatment in vitro, and in patients. In this study, we performed targeted qRT-PCR analysis of single cells using a gene panel that included PI sensitivity genes and gene signatures that could discriminate between low and high-risk myeloma followed by intensive bioinformatics and statistical analysis for the classification and prediction of PI response in individual cells within bulk multiple myeloma tumors. Fluidigm's C1 Single-Cell Auto Prep System was used to perform automated single-cell capture, processing and cDNA synthesis on 576 pre-treatment cells from 12 cell lines representing a wide range of PI-sensitivity and 370 cells from 7 patient samples undergoing PI treatment followed by targeted gene expression profiling of single cells using automated, high-throughput on-chip qRT-PCR analysis using 96.96 Dynamic Array IFCs on the BioMark HD System. Probability of resistance for each individual cell was predicted using a pipeline that employed the machine learning methods Random Forest, Support Vector Machine (radial and sigmoidal), LASSO and kNN (k Nearest Neighbor) for making single-cell GEP data-driven predictions/ decisions. The weighted probabilities from each of the algorithms were used to quantify resistance of each individual cell and plotted using Ensemble forecasting algorithm. Using our drug response GEP signature at the single cell level, we could successfully identify distinct subpopulations of tumor cells that were predicted to be sensitive or resistant to PIs. Subsequently, we developed a R Statistical analysis package (http://cran.r-project.org), SCATTome (Single Cell Analysis of Targeted Transcriptome), that can restructure data obtained from Fluidigm qPCR analysis run, filter missing data, perform scaling of filtered data, build classification models and successfully predict drug response of individual cells and classify each cell's probability of response based on the targeted transcriptome. We will present the program output as graphical displays of single cell response probabilities. This package provides a novel classification method that has the potential to predict subclonal response to a variety of therapeutic agents. Disclosures Kumar: Skyline: Consultancy, Honoraria; BMS: Consultancy; Onyx: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Novartis: Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

scholarly journals Lack of a Spleen Signal on Diffusion Weighted MRI Is Associated with High Tumor Burden and Poor Prognosis in Multiple Myeloma

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4471-4471
Author(s):  
Leo Rasche ◽  
Manoj Kumar ◽  
Grant Gershner ◽  
James E McDonald ◽  
Samrat Roy Choudhury ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Differential Gene Expression ◽  
Research Funding ◽  
Imaging Techniques ◽  
Tumor Burden ◽  
Diffusion Weighted Mri ◽  
Medical Sciences ◽  
High Tumor Burden ◽  
Differential Gene

Abstract Background: Whole body medical imaging is an integral component of Multiple Myeloma (MM) evaluation as it reveals bone disease and/or focal lesions. While established imaging techniques, such as PET-CT, are powerful in delineating focal pathologies, they suffer from low specificity when it comes to assessment of diffuse bone marrow (BM) signals. Especially in patients treated with G-CSF, discrimination between malignant and non-malignant BM signals is barely possible. Diffusion-weighted MRI with background suppression (DWIBS) is a novel functional MRI technique which measures water diffusion in vivo. Basically, diffusion of water is more restricted in tissues with high cellularity, making DWIBS a promising tool to investigate malignant diffuse signals. Initially, we searched for an internal reference to classify the level of BM infiltration. We selected the spleen, since it is the abdominal organ with the highest restriction on DWIBS. Unexpectedly, we observed significant heterogeneity in the spleen signal itself including a subgroup of MM patients with total loss of the signal on DWIBS. Perplexingly, these patients suffered from high tumor burden and poor outcome. Here, we report on our strategy to elucidate this phenomenon and to evaluate its clinical value. Methods: We investigated 295 transplant-eligible newly diagnosed MM patients and 72 cases with monoclonal gammopathy of undetermined significance (MGUS). This study was approved by the institutional review board (#205415). DWIBS was performed on a 1.5 Tesla Philips Achieva scanner. The spleen signal was assessed on DWI and ADC maps by two experienced investigators in consensus read. The Kaplan-Meier method was used for survival analyses. Molecular characterization included fluorescence in situ hybridization, and gene expression profiling of CD138-enriched plasma cells (PCs). Differential gene expression was performed using a threshold of 2-fold expression difference. Wilcoxon or Fisher's exact tests were used to compare the median of a continuous variable or the distribution of discrete variables across groups, respectively. Correlation coefficients were determined using Spearman's rank correlation. Results: Absence of the spleen signal on DWIBS was a frequent phenomenon in MM, seen in 71/295 (24%) patients. In all of these patients asplenia was excluded using alternative imaging techniques. Lack of a signal was highly positively associated with tumor-load parameters, such as the degree of BMPC infiltration (P=1x10-10) or International Staging System (ISS) 3 (P=0.0001). In contrast, it was not associated with age, gender, or the tumor progression markers gain(1q) and del(17p). Patients with absence of spleen signal experienced unfavorable outcome (hazard ratio of 1.8 for PFS and OS). In order to further investigate the biological underpinnings of this phenomenon we performed a differential gene expression analysis of purified CD138 MM cells. No differentially expressed genes were found between patients with and without spleen signal, suggesting that the absence of the spleen signal mainly reflected increased tumor burden rather than specific tumor features. As a proof of concept, we addressed the spleen signal in individuals with MGUS, and longitudinally in MM patients who presented with absence of the spleen signal at diagnoses. Indeed, in all 78 individuals with MGUS the signal was preserved, and the majority of MM patients showed re-appearance of the spleen on DWIBS during treatment as the tumor burden declined. Interestingly, re-appearance of the spleen was helpful to distinguish between malignant and non-malignant hyperintensities in the BM, making the spleen signal a promising parameter for MM follow-up investigations. Conclusions: Due to the low frequency of abnormalities affecting the spleen, this organ is often overlooked during abdominal examinations using cross-sectional imaging techniques. Here we show that the spleen signal on DWIBS provides clinically useful information on tumor burden in MM. Moreover, it opens the avenue to distinguish between malignant and reactive BM hypercellularity on imaging in patients receiving treatment. Our observation strongly supports the hypothesis, that the spleen signal is suppressed by a high BMPC involvement. Yet, the reasons for this phenomenon are still elusive. Figure. Figure. Disclosures Roy Choudhury: University of Arkansas for Medical Sciences: Employment, Research Funding. Epstein:University of Arkansas for Medical Sciences: Employment. Barlogie:Dana Farber Cancer Institute: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; Celgene: Consultancy, Research Funding; Millenium: Consultancy, Research Funding; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC; Multiple Myeloma Research Foundation: Other: travel stipend. Davies:Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Morgan:Janssen: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding.

scholarly journals Refining the adipose progenitor cell landscape in healthy and obese visceral adipose tissue using single-cell gene expression profiling

2019 ◽  
Vol 2 (6) ◽  
pp. e201900561 ◽  
Author(s):  
Dong Seong Cho ◽  
Bolim Lee ◽  
Jason D Doles
Keyword(s):  
Gene Expression ◽  
Adipose Tissue ◽  
Single Cell ◽  
Rna Sequencing ◽  
Visceral Adipose Tissue ◽  
Ex Vivo ◽  
Cellular Heterogeneity ◽  
Health Concern ◽  
Single Cell Rna Sequencing ◽  
Visceral Adipose

Obesity is a serious health concern and is associated with a reduced quality of life and a number of chronic diseases, including diabetes, heart disease, stroke, and cancer. With obesity rates on the rise worldwide, adipose tissue biology has become a top biomedical research priority. Despite steady growth in obesity-related research, more investigation into the basic biology of adipose tissue is needed to drive innovative solutions aiming to curtail the obesity epidemic. Adipose progenitor cells (APCs) play a central role in adipose tissue homeostasis and coordinate adipose tissue expansion and remodeling. Although APCs are well studied, defining and characterizing APC subsets remains ambiguous because of ill-defined cellular heterogeneity within this cellular compartment. In this study, we used single-cell RNA sequencing to create a cellular atlas of APC heterogeneity in mouse visceral adipose tissue. Our analysis identified two distinct populations of adipose tissue–derived stem cells (ASCs) and three distinct populations of preadipocytes (PAs). We identified novel cell surface markers that, when used in combination with traditional ASC and preadipocyte markers, could discriminate between these APC subpopulations by flow cytometry. Prospective isolation and molecular characterization of these APC subpopulations confirmed single-cell RNA sequencing gene expression signatures, and ex vivo culture revealed differential expansion/differentiation capabilities. Obese visceral adipose tissue featured relative expansion of less mature ASC and PA subpopulations, and expression analyses revealed major obesity-associated signaling alterations within each APC subpopulation. Taken together, our study highlights cellular and transcriptional heterogeneity within the APC pool, provides new tools to prospectively isolate and study these novel subpopulations, and underscores the importance of considering APC diversity when studying the etiology of obesity.

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